An Efficient Automated Deep Learning Model For Diatom Image Segmentation And Classification
A. Victoria Anand Mary1, G. Prabakaran2
1A. Victoria Anand Mary, Research Scholar, Department of Computer Science and Engineering, Annamalai University, India.
2G. Prabakaran, Assistant Professor, Department of Computer Science and Engineering, Annamalai University, India.
Manuscript received on 27 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 446-454 | Volume-8 Issue-11, September 2019. | Retrieval Number: K13940981119/2019©BEIESP | DOI: 10.35940/ijitee.K1394.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, diatoms, a type of algae microorganism with numerous species, are relatively helpful for water quality determination, and is treated as an important topic in applied biology nowadays. Simultaneously, deep learning (DL) also becomes an important model applied for various image classification problems. This study introduces a new Inception model for diatom image classification. The presented model involves two main stages namely segmentation and classification. Here, a deep learning based Inception model is employed for classification purposes. To further improve the classifier efficiency, edge detection based segmentation model is also applied where the segmented input is provided as an input to the classifier stage. An experimental validation takes place on diverse set of diatom dataset with various preprocessing models. The results pointed out that the presented DL model shows extraordinary classification performance with a classifier accuracy of 99%.
Keywords: Diatom; Segmentation; Classification; Deep Learning.
Scope of the Article: Deep Learning.